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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/acp-2018-174
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
07 Mar 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).
The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing
Jill S. Johnson1, Leighton A. Regayre1, Masaru Yoshioka1, Kirsty J. Pringle1, Lindsay A. Lee1, David Sexton2, John Rostron2, Ben B. B. Booth2, and Ken S. Carslaw1 1School of Earth and Environment, University of Leeds
2Met Office, Exeter, UK
Abstract. Observational constraint of simulated aerosol and cloud properties is an essential part of building trustworthy climate models for calculating aerosol radiative forcing. Models are usually tuned to achieve good agreement with observations, but tuning produces just one of many potential variants of a model, so the model uncertainty cannot be determined. Here we estimate the uncertainty in aerosol effective radiative forcing (ERF) in a tuned climate model by constraining 4 million variants of the HadGEM3-UKCA aerosol-climate model to match nine common observations (top-of-atmosphere shortwave flux, aerosol optical depth, PM2.5, cloud condensation nuclei, concentrations of sulphate, black carbon and organic carbon, as well as decadal trends in aerosol optical depth and surface shortwave radiation.) The model uncertainty is calculated by using a perturbed parameter ensemble that samples twenty-seven uncertainties in both the aerosol model and the physical climate model. Focusing over Europe, we show that the aerosol ERF uncertainty can be reduced by about 30 % by constraining it to the nine observations, demonstrating that producing climate models with an observationally plausible base state can contribute to narrowing the uncertainty in aerosol ERF. However, the uncertainty in the aerosol ERF after observational constraint is large compared to the typical spread of a multi-model ensemble. Our results therefore raise questions about whether the underlying multi-model uncertainty would be larger if similar approaches as adopted here were applied more widely. It is hoped that aerosol ERF uncertainty can be further reduced by introducing process-related constraints, however, any such results will be robust only if the enormous number of potential model variants is explored.
Citation: Johnson, J. S., Regayre, L. A., Yoshioka, M., Pringle, K. J., Lee, L. A., Sexton, D., Rostron, J., Booth, B. B. B., and Carslaw, K. S.: The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-174, in review, 2018.
Jill S. Johnson et al.
Jill S. Johnson et al.
Jill S. Johnson et al.

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Short summary
We estimate the uncertainty in an aerosol-climate model that has been tuned to match several common types of observations. We used a large set of model simulations and built emulators so that we could generate 4 million variants of our climate model. Even after using nine aerosol and cloud observations to constrain the model, the uncertainty remains large. We conclude that estimates of aerosol forcing from multi-model studies are likely to be more uncertain than currently estimated.
We estimate the uncertainty in an aerosol-climate model that has been tuned to match several...
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